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You can also try Dr. Leslie Smith’s brief online introduction to neural networks packed with pictures and examples at: http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html.

A good introductory book for managers and business analysts is:
Bigus, J.P. (1996), Data Mining with Neural Networks: Solving Business Problems--from Application Development to Decision Support, NY: McGraw-Hill.

For engineers and technically-minded people we’d recommend to start with: Fausett, L. (1994), Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Englewood Cliffs, NJ: Prentice Hall.

For financial specialists, bankers and traders we recommend starting with: E. Michael Azoff (1994). Neural Network Time Series: Forecasting of Financial Markets NY: John Wiley and Sons, Inc.

How could I improve things to get better forecasting?
You have two ways to improve results:
1) improve you input data (for more information please read Preparing Data Sets section in Advanced Issues chapter)
2) improve network topology selection and network training (for more information please read Selecting Network Topology and Training Network sections in Advanced Issues chapter).

When neural networks are a bad choice for my forecasting?
Neural networks cannot create or digest the information that is not contained in your data. To properly train a neural network you need to have a lot of data. You data should contain input parameters (signals, attributes, correlated values) that affect the target value. Change of input parameters should lead to change of target one.
So, if you have small amount of historical data or if you do not know, which parameters influence your target value, better use some other forecasting method.
In addition, there exist some problems that in principle cannot be solved by neural networks. Do not use neural networks (as well as other numerical methods) for problems like:

any problem where historical data have no use due to unbiased, rapid and significant changes in the problem environment.

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Data Analysis and Preprocessing

How much historical data do I need?
You definitely need to have more records in the training subset than the total number of input columns.
The number of records needed for training depends on the complexity of your problem and amount of noise in your data. There are no exact rules. Typically, it’s recommended to have at least 10 times as many records for training as input columns.
This may not be enough for problems with subtle and complex dependencies in data. Try to add more data if your network has poor results.

What is a categorical column?
Each value of a categorical column represents a certain category. For example, categorical is a column that contains only “Male” or “Female” as its values. Typically, the number of different values in a categorical column is much less than the number of records.
Categorical data should be encoded in a special way to be suitable for a neural network.
You may manually mark a column as categorical in Expert Mode (using Details button at Data Analysis Progress step). This feature may be beneficial for some cases. For example, your data has a column “Model” that has values “1”, “2”, “3”. By default, this column will be considered as a numeric, but it will be more beneficial to encode it as a categorical one.

Why Forecaster XL ignored some rows and columns?
This may happen if some of your columns or rows are unsuitable for neural network. For example, text or data/time data cannot be processed by neural network. Also, some your rows may have missing or invalid data; such rows will be ignored.
To see which columns and rows were ignored look into Data Preprocessing Report.

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